Entity based query filtering

ABSTRACT

In an example embodiment, one or more query terms are obtained. For each of the one or more query terms, a standardized entity taxonomy is searched to locate a standardized entity that most closely matches the query term. A confidence score is calculated for the query term-standardized entity pair for the standardized entity that most closely matches the query term. In response to a determination that the confidence score transgresses a threshold, the query term is associated with an entity identification corresponding to the standardized entity that most closely matches the query term. One or more query rewriting rules corresponding to an entity type of the standardized entity having the entity identification are obtained. The one or more query rewriting rules are executed to rewrite the first query such that the rewritten query, when performed on a data source, returns fewer search results than the first query would have.

TECHNICAL FIELD

The present disclosure generally relates to computer technology forsolving technical challenges in search queries to data sources. Morespecifically, the present disclosure relates to entity based queryfiltering.

BACKGROUND

The rise of the Internet has occasioned two disparate phenomena: theincrease in the presence of social networks, with their correspondingmember profiles visible to large numbers of people, and the increase inuse of social networks for job searches, both by applicants and byemployers. Employers, or at least recruiters attempting to connectapplicants and employers, often perform searches on social networks toidentify candidates who have qualifications that make them goodcandidates for whatever job opening they are attempting to fill. Theemployers or recruiters then can contact these candidates to see if theyare interested in applying for the job opening.

Traditional querying of social networks for candidates involves theemployer or recruiter entering one or more search terms to manuallycreate the query. A key challenge in talent searches is to translate thecriteria of a hiring position into a search query that leads to desiredcandidates. To fulfill this goal, the searcher has to understand whichskills are typically required for the position, what the alternativesare, which companies are likely to have such candidates, from whichschools the candidates are most likely to have graduated, and so forth.Moreover, the knowledge varies over time. As a result, it is notsurprising that even for experienced recruiters, it often requires manysearching trials in order to obtain a satisfactory query.

One specific problem that can occur is that traditional queryingtypically involves utilizing keyword searching, and when multiplekeywords are provided generally it is desirable to locate search resultsthat contain any of the keywords in order to increase the likelihoodthat a desired result is obtained. This can in some instances, however,cause results that have little relevance to the original query to beretrieved, especially in the employment field. For example, a search onjob listings for the terms “machine learning” may result in job listingsinvolving construction (e.g., “will need to be familiar with how tooperate heavy machines”) as well as job listings involving education(e.g., “teacher needed for intensive learning school”) that have nothingto do with the artificial intelligence “machine learning” that thesearcher intended.

It is desirable to retrieve all documents that are relevant to a query(high recall) to allow users to explore as many relevant jobs aspossible and/or allow recruiters to explore as many potential candidatesas possible, but it is also desirable to retrieve only documents thatare relevant to the query (high precision). Embarrassingly bad searchresults may show up in top positions of search results when the numberof relevant results is low. Furthermore, because facets used by users tofilter search results may be ranked by count, retrieval of large numbersof irrelevant jobs may cause facets presented to the searcher to beirrelevant as well.

BRIEF DESCRIPTION OF TIM DRAWINGS

Some embodiments of the technology are illustrated, by way of exampleand not limitation, in the figures of the accompanying drawings.

FIG. 1 is a block diagram illustrating a client-server system, inaccordance with an example embodiment.

FIG. 2 is a block diagram showing the functional components of a socialnetworking service, including a data processing module referred toherein as a search engine, for use in generating and providing searchresults for a search query, consistent with some embodiments of thepresent disclosure.

FIG. 3 is a block diagram illustrating an application server module inmore detail, in accordance with an example embodiment.

FIG. 4 is a diagram illustrating an example of the processes executed ina structuring module, in accordance with an example embodiment.

FIG. 5 is a block diagram illustrating a scoring module in more detail,in accordance with an example embodiment.

FIG. 6 is a flow diagram illustrating a method for using a standardizedentity taxonomy for query rewriting, in accordance with an exampleembodiment.

FIG. 7 is a block diagram illustrating a representative softwarearchitecture, which may be used in conjunction with various hardwarearchitectures herein described.

FIG. 8 is a block diagram illustrating components of a machine,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.

DETAILED DESCRIPTION

Overview

The present disclosure describes, among other things, methods, systems,and computer program products that individually provide variousfunctionality. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding of the various aspects of different embodimentsof the present disclosure. It will be evident, however, to one skilledin the art, that the present disclosure may be practiced without all ofthe specific details.

In an example embodiment, a system is provided whereby entities areidentified in a search query and standardized identifications for theentities are obtained. The standardized identifications may be stored ina standardized entity taxonomy. The query entities are then tagged withthese standardized identifications. These standardized identificationscan then be used to identify related entities in the standardized entitytaxonomy. These related entities can be used for a variety of purposes,including query rewriting, result filtering, and result ranking. In anexample embodiment, query rewriting is performed to restrict searchresults based on one or more rules provided for each type of entity inthe query.

Via semantic query representation, the searcher's intent for the querycan be determined. For example, the query “machine learning” is not justviewed as a series or even a sequence of keywords, but is insteadrecognized as a “skill” type entity in the taxonomy. By understandingthe query at the entity level, it becomes possible to rewrite the queryto only retrieve relevant results to the searcher's query, filtering outjobs that may have those keywords in the text but are completelyillogical. In an example embodiment, only documents with a high semanticsimilarity with the query will be kept as search results.

FIG. 1 is a block diagram illustrating a client-server system 100, inaccordance with an example embodiment. A networked system 102 providesserver-side functionality via a network 104 (e.g., the Internet or awide area network (WAN)) to one or more clients. FIG. 1 illustrates, forexample, a web client 106 (e.g., a browser) and a programmatic client108 executing on respective client machines 110 and 112.

An application program interface (API) server 114 and a web server 116are coupled to, and provide programmatic and web interfaces respectivelyto, one or more application servers 118. The application server(s) 118host one or more applications 120. The application server(s) 118 are, inturn, shown to be coupled to one or more database servers 124 thatfacilitate access to one or more databases 126. While the application(s)120 are shown in FIG. 1 to form part of the networked system 102, itwill be appreciated that, in alternative embodiments, the application(s)120 may form part of a service that is separate and distinct from thenetworked system 102.

Further, while the client-server system 100 shown in FIG. 1 employs aclient-server architecture, the present disclosure is, of course, notlimited to such an architecture, and could equally well find applicationin a distributed, or peer-to-peer, architecture system, for example. Thevarious applications 120 could also be implemented as standalonesoftware programs, which do not necessarily have networkingcapabilities.

The web client 106 accesses the various applications 120 via the webinterface supported by the web server 116. Similarly, the programmaticclient 108 accesses the various services and functions provided by theapplication(s) 120 via the programmatic interface provided by the APIserver 114.

FIG. 1 also illustrates a third-party application 128, executing on athird-party server 130, as having programmatic access to the networkedsystem 102 via the programmatic interface provided by the API server114. For example, the third-party application 128 may, utilizinginformation retrieved from the networked system 102, support one or morefeatures or functions on a website hosted by a third party. Thethird-party website may, for example, provide one or more functions thatare supported by the relevant applications 120 of the networked system102.

In some embodiments, any website referred to herein may comprise onlinecontent that may be rendered on a variety of devices including, but notlimited to, a desktop personal computer (PC), a laptop, and a mobiledevice (e.g., a tablet computer, smartphone, etc.). In this respect, anyof these devices may be employed by a user to use the features of thepresent disclosure. In some embodiments, a user can use a mobile app ona mobile device (any of the client machines 110, 112 and the third-partyserver 130 may be a mobile device) to access and browse online content,such as any of the online content disclosed herein. A mobile server(e.g., API server 114) may communicate with the mobile app and theapplication server(s) 118 in order to make the features of the presentdisclosure available on the mobile device.

In some embodiments, the networked system 102 may comprise functionalcomponents of a social networking service. FIG. 2 is a block diagramshowing the functional components of a social networking service,including a data processing module referred to herein as a search engine216, for use in generating and providing search results for a searchquery, consistent with some embodiments of the present disclosure. Insome embodiments, the search engine 216 may reside on the applicationserver(s) 118 in FIG. 1. However, it is contemplated that otherconfigurations are also within the scope of the present disclosure.

As shown in FIG. 2, a front end may comprise a user interface module(e.g., a web server 116) 212, which receives requests from variousclient computing devices and communicates appropriate responses to therequesting client devices. For example, the user interface module(s) 212may receive requests in the form of Hypertext Transfer Protocol (HTTP)requests or other web-based API requests. In addition, a memberinteraction detection module 213 may be provided to detect variousinteractions that members have with the different applications 120,services, and content presented. As shown in FIG. 2, upon detecting aparticular interaction, the member interaction detection module 213 logsthe interaction, including the type of interaction and any metadatarelating to the interaction, in a member activity and behavior database222.

An application logic layer may include one or more various applicationserver modules 214, which, in conjunction with the user interfacemodule(s) 212, generate various user interfaces (e.g., web pages) withdata retrieved from various data sources in a data layer. In someembodiments, individual application server modules 214 are used toimplement the functionality associated with various applications 120and/or services provided by the social networking service.

As shown in FIG. 2, the data layer may include several databases, suchas a profile database 218 for storing profile data, including bothmember profile data and profile data for various organizations (e.g.,companies, schools, etc.). Consistent with some embodiments, when aperson initially registers to become a member of the social networkingservice, the person will be prompted to provide some personalinformation, such as his or her name, age (e.g., birthdate), gender,interests, contact information, home town, address, spouse's and/orfamily members' names, educational background (e.g., schools, majors,matriculation and/or graduation dates, etc.), employment history,skills, professional organizations, and so on. This information isstored, for example, in the profile database 218. Similarly, when arepresentative of an organization initially registers the organizationwith the social networking service, the representative may be promptedto provide certain information about the organization. This informationmay be stored, for example, in the profile database 218, or anotherdatabase (not shown). In some embodiments, the profile data may beprocessed (e.g., in the background or offline) to generate variousderived profile data. For example, if a member has provided informationabout various job titles that the member has held with the sameorganization or different organizations, and for how long, thisinformation can be used to inter or derive a member profile attributeindicating the member's overall seniority level, or seniority levelwithin a particular organization. In some embodiments, importing orotherwise accessing data from one or more externally hosted data sourcesmay enrich profile data for both members and organizations. Forinstance, with organizations in particular, financial data may beimported from one or more external data sources and made part of anorganization's profile. This importation of organization data andenrichment of the data will be described in more detail later in thisdocument.

Once registered, a member may invite other members, or be invited byother members, to connect via the social networking service. A“connection” may constitute a bilateral agreement by the members, suchthat both members acknowledge the establishment of the connection.Similarly, in some embodiments, a member may elect to “follow” anothermember. In contrast to establishing a connection, the concept of“following” another member typically is a unilateral operation and, atleast in some embodiments, does not require acknowledgement or approvalby the member who is being followed. When one member follows another,the member who is following may receive status updates (e.g., in anactivity or content stream) or other messages published by the memberbeing followed, or relating to various activities undertaken by themember being followed. Similarly, when a member follows an organization,the member becomes eligible to receive messages or status updatespublished on behalf of the organization. For instance, messages orstatus updates published on behalf of an organization that a member isfollowing will appear in the member's personalized data feed, commonlyreferred to as an activity stream or content stream. In any case, thevarious associations and relationships that the members establish withother members, or with other entities and objects, are stored andmaintained within a social graph in a social graph database 220.

As members interact with the various applications 120, services, andcontent made available via the social networking service, the members'interactions and behavior (e.g., content viewed, links or buttonsselected, messages responded to, etc.) may be tracked, and informationconcerning the members' activities and behavior may be logged or stored,for example, as indicated in FIG. 2, by the member activity and behaviordatabase 222. This logged activity information may then be used by thesearch engine 216 to determine search results for a search query.

In some embodiments, the databases 218, 220, and 222 may be incorporatedinto the database(s) 126 in FIG. 1. However, other configurations arealso within the scope of the present disclosure.

Although not shown, in some embodiments, the social networking system210 provides an API module via which applications 120 and services canaccess various data and services provided or maintained by the socialnetworking service. For example, using an API, an application 120 may beable to request and/or receive one or more navigation recommendations.Such applications 120 may be browser-based applications 120, or may beoperating system-specific. In particular, some applications 120 mayreside and execute (at least partially) on one or more mobile devices(e.g., phone or tablet computing devices) with a mobile operatingsystem. Furthermore, while in many cases the applications 120 orservices that leverage the API may be applications 120 and services thatare developed and maintained by the entity operating the socialnetworking service, nothing other than data privacy concerns preventsthe API from being provided to the public or to certain third partiesunder special arrangements, thereby making the navigationrecommendations available to third-party applications 128 and services.

Although the search engine 216 is referred to herein as being used inthe context of a social networking service, it is contemplated that itmay also be employed in the context of any website or online services.Additionally, although features of the present disclosure are referredto herein as being used or presented in the context of a web page, it iscontemplated that any user interface view (e.g., a user interface on amobile device or on desktop software) is within the scope of the presentdisclosure.

In an example embodiment, when member profiles are indexed, forwardsearch indexes are created and stored. The search engine 216 facilitatesthe indexing and searching for content within the social networkingservice, such as the indexing and searching for data or informationcontained in the data layer, such as profile data (stored, e.g., in theprofile database 218), social graph data (stored, e.g., in the socialgraph database 220), and member activity and behavior data (stored,e.g., in the member activity and behavior database 222). The searchengine 216 may collect, parse, and/or store data in an index or othersimilar structure to facilitate the identification and retrieval ofinformation in response to received queries for information. This mayinclude, but is not limited to, forward search indexes, invertedindexes, N-gram indexes, and so on.

FIG. 3 is a block diagram illustrating the application server module 214of FIG. 2 in more detail. While in many embodiments the applicationserver module 214 will contain many subcomponents used to performvarious different actions within the social networking system 210, inFIG. 3 only those components that are relevant to the present disclosureare depicted. A query structuring system 300 includes a communicationmodule 310, a structuring module 320, a scoring module 330, a rewritingmodule 340, a ranking module 350, and an indexing module 360.

The communication module 310 is configured to perform variouscommunication functions to facilitate the functionality describedherein. For example, the communication module 310 may communicate withusers via the network 104 using a wired or wireless connection. Thecommunication module 310 may also provide various web services functionssuch as retrieving information from the third-party servers 130 and thesocial networking system 210. In this way, the communication module 310facilitates the communication of the query structuring system 300 withthe client machines 110, 112 and the third-party servers 130 via thenetwork 104. Information retrieved by the communication module 310 mayinclude profile data corresponding to the user and other members of thesocial network service from the social networking system 210. Asdepicted, the communication module 310 is further configured to receivean input query to perform a search on information, including, but notlimited to, member profiles. The input query may be received via afront-end interface, such as a web page rendered in a web browser or adedicated client application. Regardless of how the input query isobtained, it can be passed to the structuring module 320 for furtherprocessing prior to the query being executed on whatever relevantdatabase(s) can fulfill the query.

The structuring module 320 is configured to generate, from an inputquery, a tagged version of the query that includes information aboutstandardized portions (called “entities”) of the query. A standardizedentity taxonomy 312 may be referenced during this process. Thestandardized entity taxonomy 312 may include an indication of variousstandardized entities and corresponding entity identifications (such asunique numbers corresponding to each entity). The standardized entitytaxonomy 312 may include various portions devoted to different taxonomycategories, such as, for example, a titles portion 314A, a companyportion 314B, a skills portion 314C, a location portion 314D, and aschools portion 314E. In other embodiments, each of these portions314A-314E may be stored as its own independent taxonomy.

In some example embodiments, the standardized entity taxonomy 312 maycomprise a data structure that includes different levels of a hierarchy,such as a tree graph. This allows some of the standardized entities tobe parents or children of other standardized entities, reflecting ahierarchical relationship between them. For example, the titles of“software engineer” and “software developer” both may be children nodesof a higher-level title known as “computer scientist.”

The standardized entity taxonomy 312 may be stored in, for example, aDistributed File System (DFS) 316.

The structuring module 320 may, for example, receive an input query of“software engineer,” and map the term “software engineer” to thestandardized term “Software Engineer” with a title identification (ID)(e.g., 21) within the standardized entity taxonomy 312. The entity“software engineer” in the query can then be tagged with this title ID(21). Additionally, a confidence score can be obtained for this titleID. This confidence score reflects the likelihood that a user havingsearched the term “software engineer” intended to search for the titleof “Software Engineer.” As will be described later, the confidence scoremay be generated by a confidence score model created through a machinelearning algorithm. The entity in the query can also be tagged with thisconfidence score.

Thus, in various embodiments, the tagged query also encapsulatessemantic ambiguity inherent within the input query. Within shortqueries, there is often not enough surrounding context to determine thecorrect choice when it comes to several interpretations of a singleword. As will be seen, a query may eventually be modified to representsuch ambiguities and synonyms by representing the query in all itspossible interpretations. Each interpretation of an ambiguity isassociated with a confidence score calculated by the scoring module 330,as discussed in further detail below.

The initial tagged query can be passed as input to various othermodules, including the rewriting module 340 and the ranking module 350.

In the rewriting module 340, the tagged raw query may be augmentedthrough various mechanisms. First, the initial tagging can be augmentedby adding Boolean keywords, which will be useful when additionalentities are added in a subsequent step. Thus, terms like “AND” and “OR”can be added to the query. At this point, additional entities can beadded to the query based on confidence scores assigned to thoseadditional entities, as generated using the standardized entities in thequery. Thus, for example, if the query has been tagged with thestandardized title ID of 21, then additional titles (e.g., “SoftwareDeveloper” with a title ID 22) may also be added, if the confidencescores so indicate. Additionally, the standardized entities themselvescan be added to the query.

FIG. 4 is a diagram illustrating an example of the processes executed inthe structuring module 320, in accordance with an example embodiment.Here, a user query 400 may be “linkedin software engineer.” A querytagger 402 may then identify that “linkedin” bears a strong resemblanceto a company entity in the company portion 314B of the standardizedentity taxonomy 312 having a standardized company name of “LinkedIn,”and the scoring module 330 calculates the confidence score as 0.9(representing the likelihood that the user's typing of the term“linkedin” meant the standardized company name “LinkedIn”). Thestandardized company identification (1337) and the confidence score canthen be tagged in the query. Likewise, the term “software engineer” inthe query may be mapped to the standardized title “Software Engineer” inthe title portion 314A of the standardized entity taxonomy 312. Thestandardized title identification for “Software Engineer” (21) and theconfidence score (0.8) can be tagged to this term in the query. Theresult is a tagged raw query 404. It should be noted that the tagged rawquery 404 may also include an indication of the entity type for eachterm, here depicted as “C” for company name and “T” for title. This maybe helpful in the later execution of the query, as search results can besearched based on these entity types. Thus, for example, rather thanlooking in all fields of a search result for “linkedin,” only thecompany name field may be searched, thereby reducing processing time.

Advanced keywords 406 can then be added to the tagged raw query 404 tointroduce Boolean logic terms into the query. Here, for example, an ANDmay be added to the tagged raw query 404 in light of the fact that boththe terms on either side of the AND were explicitly entered as searchterms by the user.

Then standardized entities 408 and per entity synonyms 410 can be addedas metadata annotations 412 to the tagged raw query 404. Standardizedentities 408 are the identifications of the standardized entities addedearlier. This step involves breaking out those identifications asindependent search query terms and linking them to the original searchquery term via an OR connector. Per entity synonyms 410 includeadditional standardized entity names and identifications that have beenpreviously identified as synonyms of query terms and/or standardizedentities in the tagged raw query 404.

It should be noted that the confidence scores for each of thestandardized entities added to the tagged raw query 404 can be used aspart of the metadata annotations 412 process in order to decide whetherto actually add each standardized entity identification to the taggedraw query 404. This may be accomplished using, for example, a confidencescore threshold. Each confidence score can be compared to the threshold,and if the confidence score transgresses the threshold, then thecorresponding standardized entity identification may be added as ametadata annotation 412.

Thus, in the example in FIG. 4, assume the confidence score threshold is0.7. Since both 0.9 (for standardized company identification 1337) and0.8 (for standardized title identification 21) transgress thisthreshold, both these identifications may be added to the tagged rawquery 404. The result is the Boolean expression (C: LinkedIn OR Cid:1337) AND ((T: Software Engineer OR Tid: 21) OR (T: Software DeveloperOR Tid: 22)). “Software Developer” is a predetermined synonym for“Software Engineer,” and thus is added as a per entity synonym 410.

Then, various vertical specific additions 414 may be added to the query.Vertical specific additions allow for different granularities of asearch term to be explored, based on the entity type. For example, aparticular job title may be associated with various job functions. Thesejob functions may be stored in the standardized entity taxonomy 312 aschild nodes of the job title node. The rewriting module 340 may exploreadding these child job functions as additional query terms based onconfidence scores assigned to these child job functions (e.g., thelikelihood that a user typing a specific title actually means specificjob functions and does not mean other specific job functions).

Thus, for example, a search on the term “machine learning” may result inthe determination that this term corresponds to a skill “machinelearning” with a confidence score of 0.99 (lowest being 0.00 and highestbeing 1.00). The skill entity identification for “machine learning” inthe standardized entity taxonomy 312 may be, for example, 217. As such,the query “machine learning” may be rewritten from “machine OR learning”to “(machine OR learning) OR standardizedSkills: 217).

As described earlier, the rewriting performed by the rewriting module340 may additionally or alternatively include the addition of searchterms or criteria to restrict the search results based on one or morerules corresponding to the entity types. In the above example, the querymay be rewritten to “(machine OR learning) AND standardizedSkills: 217”in order to restrict the search results to only those results having themachine learning entity as a skill. The inclusion of the entity itselfas part of the query, using an AND connector, is only one simple rulethat can be implemented to rewrite the query based on the entity type(s)(here the rule being “include the standardized entity with the queryusing an AND connector”). Additional, more complex rules can also bespecified for each entity type.

For example, the structuring module 320 may not be perfect when it comesto mapping query terms to skills. Recognizing this, an additional rulefor skills entities may be check to see whether a phrase (i.e., morethan one word) appears in the query and, if so, perform a keyword searchfor that phrase as a whole rather than as individual words. Thus, thequery example above may be rewritten as “(machine OR learning) ANDstandardizedSkills: 217 AND jobBody: ‘machine learning.’”

An additional rule that could be specified for skills entities is onethat recognizes that there may be a correlation between titles andskills. Information on these correlations may be maintained in ataxonomy, and then used to obtain titles that have a high affinity witha particular skill. For example, a skill like machine learning wouldhave a high title affinity with data scientists, software engineers, andthe like. This information is then used to retrieve search results witha standardized title that has a high affinity with the query skill. Ifthe search results' standardized title has a high affinity with thequery skill, then this indicates that the keyword match was not a fluke.In an example embodiment, affinity scores are calculated using a g-scorestatistic. Specifically, a g-score statistic is calculated using ag-test of independence. A g-test is a likelihood ratio or maximumlikelihood statistical significance test using a formula such as:

$G = {2{\sum_{i}{O_{i} \cdot {\ln\left( \frac{O_{i}}{E_{i}} \right)}}}}$where O_(i) is the observed count, E_(i) is the expected count under thenull hypothesis. In denotes the natural logarithm, and the sum is takenover all non-empty values.

Thus, for example, in the case of titles at a particular company, thereare multiple title buckets for the company that are formed. For eachbucket, all of the skills of these members are recorded and the numberof members for each skill in each bucket is counted. For example, forthe bucket “software engineer” at LinkedIn, with the skill Java, thenumber of members in this bucket having the skill Java is counted. Thenthe null hypothesis that Java is distributed randomly across differenttitle/company buckets is assumed, meaning there is independence betweenJava as a skill and software engineers at LinkedIn (an alternativehypothesis being that there is a dependency). A contingency table forsuch an example is constructed as depicted in Table 1 below:

TABLE 1 Software Engineer @ Not Software Engineer @ LinkedIn LinkedInHave Java as Skill 460 2280255 Don't Have Java 116 716523340 as Skill

This table is then used to calculate the g-value using the aboveformula, which comes to 2630.745, meaning a high affinity between theskill and the job title. A threshold value may be utilized to determinewhether or not a title has a high affinity with the skill, and if so,the standardized title may be added to the query along with the ANDconnector. Thus, the rule may be “determine if there are anystandardized titles having an affinity score with the standardized skillhigher than a preset threshold, and include any such standardized titlesin the query with an AND connector.” In the above example, the query maybe rewritten as “(machine OR learning) AND standardizedSkills: 217 ANDjobBody: ‘machine learning’ AND standardizedTitle: 9,” where thestandardized title “Software Engineer” has a title identification of 9.

An additional rule that could be specified for skills entities is onethat recognizes that skills may be related to each other, and that thisrelation can be used to limit bare keyword searches. It should be notedthat this rule likely would be used in conjunction with the rule aboveindicating that the standardized skill should be directly included inthe query such that the skills are presented as connected with ORconnectors, albeit in the context of all the skills being connected tothe keyword search or other portions of the query with an AND connector.Thus, for example, if it is determined that the standardized skill of“data mining,” having an identification of 835, is similar to thestandardized skill of “machine learning,” then the original query“(machine OR learning)” may be rewritten as “(machine OR learning) AND(standardizedSkills: 217 OR standardizedSkills: 835).”

Similarity between skills may be measured based on affinity scorescalculated using pointwise mutual information (PMI). Assuming log base2, a PMI value of 3 indicates that the probability of a second skillgiven a first skill is 8 times more likely than the probability of thesecond skill alone. The reverse is also true for PMI. In an exampleembodiment, a PMI value of 3 is used as the threshold at which thesecond skill is deemed to be similar enough to the first skill to beincluded in the query rewriting rule.

Skill affinity may be calculated by looking at all of the explicitskills in a member profile. Using all of the explicit skill data, theprobability of each of the skills being present (number of members withthe skill divided by total number of members) may be calculated.Additionally, each of the conditional probabilities p(skill1|skill2)(number of members with skill1 and skill2 divided by number of memberswith skill 2) may be calculated. With these probabilities, PMI may becalculated to derive the affinity score between skill1 and skill2.

The PMI of a pair of outcomes x and y belonging to discrete randomvariables X, Y, and Z quantifies the discrepancy between the probabilityof their coincidence given their joint distributions and theirindividual distributions, assuming independence. Mathematically this is:

${{{pmi}\left( {x;y} \right)} \equiv {\log\frac{p\left( {x,y} \right)}{{p(x)}{p(y)}}}} = {{\log\frac{p\left( {x\text{|}y} \right)}{p(x)}} = {\log\frac{p\left( {y\text{|}z} \right)}{p(y)}}}$

As with skill entities, any title entities in the query may have thesimple rule of inclusion of the standardized title in the query using anAND connector. Thus, a query of “engineering manager” could be rewrittenas “(engineering OR manager) AND standardizedTitles: 174” (assuming thatthe standardized title “Engineering Manager” has an identification of174 in the taxonomy).

An alternative rule may genericize the standardized title up to astandardized function level. Specifically, the taxonomy may behierarchical, such that each generic function, such as Engineering, maybe associated with multiple standardized titles. Thus, for theparticular standardized title “Engineering Manager” the associatedstandardized function “Engineering” may be used, having anidentification of 8. As such, the query may be rewritten to“(engineering OR manager) AND standardizedFunction: 8.”

Additionally, as in the case of skills, the structuring module 320 maynot be perfect when it comes to mapping query terms to titles. Unlikeskills, however, which can occur anywhere in a search result, typicallya dedicated text field is provided for title. For maximum recall, only asingle token match may be needed for retrieval of job titles, but thiscan be extended to enforce phrase matching if higher precision isdesired.

Of course, the query may be made up of multiple different types ofentities. For example, the searcher may have searched on “engineeringmanager machine learning.” The structuring module 320 is able todetermine that this query is comprised of the different entities“engineering manager” and “machine learning” and rewrite the query intotal using the different rules for the different entity types. Thequery may be rewritten as “((machine OR learning) AND(standardizedSkills: 217 OR standardizedSkills: 835)) OR ((engineeringOR manager) AND standardizedFunction: 8).”

In some embodiments, the rewritten query is presented to the user andthe user may alter the input query to clarify any ambiguity. In someembodiments, any clarification added by the user subsequent to theinitial query is added to the existing generated data structure. Forinstance, if the user's initial query is “linkedin software engineer,”then subsequently, after a search result is returned for that initialquery, the user may add in the word “company,” resulting in the secondquery “linkedin company software engineer” to clarify any ambiguitybetween the company “linkedin” and another type of entity called“linkedin,” such as a skill.

The result of this entire process is a final rewritten query 416.

Referring back to FIG. 3, the rewritten query may then be passed fromthe structuring module 320 to a query processor (not pictured) thatperforms a search on the query and returns search results to the rankingmodule 350. While not pictured in FIG. 3, in some example embodiments,these communications may be passed through the communication module 310.

In various embodiments, the ranking module 350 is configured to rankdocuments retrieved in response to a search query in an order ofrelevance based on various factors, including, for example, the match ofthe input query to the information within a document, personalinformation within the member profile of the searcher or result, and/orinformation pertaining to the professional network of the searcher orresult. Each factor that influences the ranking order of the retrieveddocuments has an associated predetermined weight, with the documentscoring higher based on these predetermined weights being ranked higher.For example, first connections may be weighted more than secondconnections, and so forth, where a first connection refers to the userbeing directly connected to a second member profile. A second connectionrefers to the user being directly connected to another member's profile,who is then directly connected to the second member profile. In anotherexample, member profiles that share similarities with the user's profileare weighted more than other member profiles that have fewersimilarities.

In an example embodiment, the ranking module 350 uses a multipass scoreron results documents. At each pass, the search results are filtered anddowngraded based on entity-based features from, for example, the taggedraw query 404 and/or the final rewritten query 416.

Another component that can utilize the standardized entity taxonomy 312is the indexing module 360. Offline indexing can be used periodically toindex new documents, profiles, and other information in the database.The standardized entity taxonomy 312 may be utilized during thisindexing time to aid in the indexing process. For example, a processsimilar to query tagging can occur with various fields in the document.If a member profile, for example, lists a particular title that themember has entered for him or herself, then this profile may be indexednot just by the provided title, but by an identification of a mappedstandardized title entity corresponding to that provided title, as wellas by synonyms of or titles related to the provided title.

In some implementations, a presentation module (not pictured) isconfigured to present query rewriting recommendations to the user,present search results according to their ranked order, present a reasonassociated with why the query result is being presented (e.g., such as ashared connection), and present the search results withcategory-selected highlighting. In some embodiments, where there areambiguities associated with a word, the interpretation associated withretrieving a result is shown to the user. In various implementations,the presentation module presents or causes presentation of information(e.g., information visually displayed on a screen, acoustic output,haptic feedback). Interactively presenting information is intended toinclude the exchange of information between a particular device and theuser of that device. The user of the device may provide input tointeract with a user interface in many possible manners, such asalphanumeric input, point based (e.g., cursor) input, tactile input, orother input (e.g., touch screen, tactile sensor, light sensor, infraredsensor, biometric sensor, microphone, gyroscope, accelerometer, or othersensors), and the like. It will be appreciated that the presentationmodule provides many other user interfaces to facilitate functionalitydescribed herein. Further, it will be appreciated that “presenting” asused herein is intended to include communicating information orinstructions to a particular device that is operable to performpresentation based on the communicated information or instructions viathe communication module 310, structuring module 320, scoring module330, rewriting module 340, ranking module 350, and indexing module 360.

As described earlier, the scoring module 330 is configured to determinea confidence score associated with each possible entity of the inputquery. An input query may have inherent semantic ambiguities andsynonyms associated with some of the key words within the query. Theconfidence score indicates the accuracy with which the system maps eachterm to a corresponding standardized entity, based on the likelihoodthat the searcher, under ideal circumstances, would have specified thestandardized entity in the query.

In an example embodiment, the confidence score is calculated based onmachine learning models of two types of training data sets, includingpast activities of all members from the member activity and behaviordatabase 222 and the profile data of all members from the profiledatabase 218. The confidence score is calculated based on memberactivity data indicating a percentage of member activity associating theword term to the corresponding standardized entity. For instance, memberactivities and behavior include statistics showing when users type inthe same word terms as an input query and the corresponding frequencywith which the users then click on search results with one of theinterpretations of the known ambiguity. Continuing with the previousexample, when users input a search query with the word term “linkedin,”the scoring module 330 determines that 70% of the time, the users thenclick on search results that specify LinkedIn as the company ratherthan, for example, LinkedIn as a skill or location. In this instance,the confidence score of assigning the category “company” to the wordterm “linkedin” is 0.7.

In other embodiments, in determining the confidence score associatedwith assigning a word term within an input query to a specificstandardized entity, the scoring module 330 uses profile data of membersobtained from the profile database 218. The confidence score iscalculated based on member profile data indicating a percentage ofmember profile data associating the word term to the correspondingstandardized entity. For instance, statistics are determined from memberprofiles in order to determine the category in which the word term canbe found. Continuing with the previous example, the scoring module 330determines that 0.001% of the profiles within the profile database 218indicate that LinkedIn is a skill set. In this instance, the confidencescore of mapping the query term “linkedin” to a skill of “LinkedIn” is0.001.

In other embodiments, the confidence score is calculated based on bothmember activity data and member profile data.

FIG. 5 is a block diagram illustrating a scoring module 330 in moredetail, in accordance with an example embodiment. The scoring module 330may utilize machine learning processes to arrive at a scoring model 500used to provide a confidence score for a particular queryterm-standardized entity pair. The scoring module 330 may comprise atraining component 502 and a confidence scoring component 504. Thetraining component 502 feeds training data 506 comprising, for example,member profile data and member activity data into a feature extractor508 that extracts one or more features 510 of the information. Thefeatures 510 are statistical measurements useful in determining whethera member searching on a particular query term actually meant to searchon the particular standardized entity being analyzed. A machine learningalgorithm 512 produces the scoring model 500 using the extractedfeatures 510. In the confidence scoring component 504, candidate queryterm-standardized entity pairs are fed to the scoring model 500, whichoutputs a confidence score for each pair based on the scoring model 500.

It should be noted that the scoring model 500 may be periodicallyupdated via additional training and/or user feedback. The user feedbackmay be either feedback from members performing searches or fromadministrators. The feedback may include an indication about howsuccessful the scoring model 500 is in providing accurate confidencescores.

The machine learning algorithm 512 may be selected from among manydifferent potential supervised or unsupervised machine learningalgorithms. Examples of supervised learning algorithms includeartificial neural networks, Bayesian networks, instance-based learning,support vector machines, random forests, linear classifiers, quadraticclassifiers, k-nearest neighbor, decision trees, and hidden Markovmodels. Examples of unsupervised learning algorithms includeexpectation-maximization algorithms, vector quantization, andinformation bottleneck method. In an example embodiment, a multi-classlogistical regression model is used.

As described above, the training component 502 may operate in an offlinemanner to train the scoring model 500. The confidence scoring component504, however, may be designed to operate in either an offline manner oran online manner.

FIG. 6 is a flow diagram illustrating a method 600 for using astandardized entity taxonomy for query rewriting, in accordance with anexample embodiment. At operation 602, one or more query terms in a firstquery are obtained. These query terms may be obtained, for example, aspart of a search query entered by a member of a social networkingservice. Then a loop is begun for each of the one or more query terms.At operation 604, a standardized entity taxonomy is searched to locate astandardized entity that most closely matches the query term. Then, atoperation 606, a confidence score is calculated for the queryterm-standardized entity pair for the standardized entity that mostclosely matches the query term. Then, at operation 608, it is determinedif the confidence score transgresses a threshold. If not, then themethod 600 may advance to operation 616. If so, however, then the method600 proceeds to operation 610.

At operation 610, the query term is associated with the entityidentification. At operation 612, one or more query rewriting rulescorresponding to the entity type of the standardized entity having theentity identification are retrieved. At operation 614, the one or morequery rewriting rules are executed to rewrite the first query such thatrewritten query is more restrictive than the first query (i.e., therewritten query, when performed on a data source, returns fewer searchresults than the first query would have on the same data source).

At operation 616, it may be determined if there are any more queryterms. If so, then the method 600 loops back to operation 604 for thenext query term. If not, then the method 600 ends. It should be notedthat while a specific ordering of operations within the loop ispresented in this figure, alterations of this figure are possible wheremultiple loops are performed independently. For example, operations604-608 may be performed for each query term, and then operations610-614 may be performed in a separate loop for each query term.

Modules, Components, and Logic

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules may constitute eithersoftware modules (e.g., code embodied on a machine-readable medium) orhardware modules. A “hardware module” is a tangible unit capable ofperforming certain operations and may be configured or arranged in acertain physical manner. In various example embodiments, one or morecomputer systems (e.g., a standalone computer system, a client computersystem, or a server computer system) or one or more hardware modules ofa computer system (e.g., a processor or a group of processors) may beconfigured by software (e.g., an application or application portion) asa hardware module that operates to perform certain operations asdescribed herein.

In some embodiments, a hardware module may be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware module may include dedicated circuitry or logic that ispermanently configured to perform certain operations. For example, ahardware module may be a special-purpose processor, such as aField-Programmable Gate Array (FPGA) or an Application SpecificIntegrated Circuit (ASIC). A hardware module may also includeprogrammable logic or circuitry that is temporarily configured bysoftware to perform certain operations. For example, a hardware modulemay include software executed by a general-purpose processor or otherprogrammable processor. Once configured by such software, hardwaremodules become specific machines (or specific components of a machine)uniquely tailored to perform the configured functions and are no longergeneral-purpose processors. It will be appreciated that the decision toimplement a hardware module mechanically, in dedicated and permanentlyconfigured circuitry, or in temporarily configured circuitry (e.g.,configured by software) may be driven by cost and time considerations.

Accordingly, the phrase “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. As used herein,“hardware-implemented module” refers to a hardware module. Consideringembodiments in which hardware modules are temporarily configured (e.g.,programmed), each of the hardware modules need not be configured orinstantiated at any one instance in time. For example, where a hardwaremodule comprises a general-purpose processor configured by software tobecome a special-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware modules) at different times. Softwareaccordingly configures a particular processor or processors, forexample, to constitute a particular hardware module at one instance oftime and to constitute a different hardware module at a differentinstance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multiplehardware modules exist contemporaneously, communications may be achievedthrough signal transmission (e.g., over appropriate circuits and buses)between or among two or more of the hardware modules. In embodiments inwhich multiple hardware modules are configured or instantiated atdifferent times, communications between such hardware modules may beachieved, for example, through the storage and retrieval of informationin memory structures to which the multiple hardware modules have access.For example, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions describedherein. As used herein, “processor-implemented module” refers to ahardware module implemented using one or more processors.

Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors orprocessor-implemented modules. Moreover, the one or more processors mayalso operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an API).

The performance of certain of the operations may be distributed amongthe processors, not only residing within a single machine, but deployedacross a number of machines. In some example embodiments, the processorsor processor-implemented modules may be located in a single geographiclocation (e.g., within a home environment, an office environment, or aserver farm). In other example embodiments, the processors orprocessor-implemented modules may be distributed across a number ofgeographic locations.

Machine and Software Architecture

The modules, methods, applications, and so forth described inconjunction with FIGS. 1-6 are implemented in some embodiments in thecontext of a machine and an associated software architecture. Thesections below describe representative software architecture(s) andmachine (e.g., hardware) architectures) that are suitable for use withthe disclosed embodiments.

Software architectures are used in conjunction with hardwarearchitectures to create devices and machines tailored to particularpurposes. For example, a particular hardware architecture coupled with aparticular software architecture will create a mobile device, such as amobile phone, tablet device, or so forth. A slightly different hardwareand software architecture may yield a smart device for use in the“internee of things,” while yet another combination produces a servercomputer for use within a cloud computing architecture. Not allcombinations of such software and hardware architectures are presentedhere, as those of skill in the art can readily understand how toimplement the inventive subject matter in different contexts from thedisclosure contained herein.

Software Architecture

FIG. 7 is a block diagram 700 illustrating a representative softwarearchitecture 702, which may be used in conjunction with various hardwarearchitectures herein described. FIG. 7 is merely a non-limiting exampleof a software architecture, and it will be appreciated that many otherarchitectures may be implemented to facilitate the functionalitydescribed herein. The software architecture 702 may be executing onhardware such as a machine 800 of FIG. 8 that includes, among otherthings, processors 810, memory/storage 830, and I/O components 850. Arepresentative hardware layer 704 is illustrated and can represent, forexample, the machine 800 of FIG. 8. The representative hardware layer704 comprises one or more processing units 706 having associatedexecutable instructions 708. The executable instructions 708 representthe executable instructions of the software architecture 702, includingimplementation of the methods, modules, and so forth of FIGS. 1-6. Thehardware layer 704 also includes memory and/or storage modules 710,which also have the executable instructions 708. The hardware layer 704may also comprise other hardware 712, which represents any otherhardware of the hardware layer 704, such as the other hardwareillustrated as part of the machine 800.

In the example architecture of FIG. 7, the software architecture 702 maybe conceptualized as a stack of layers where each layer providesparticular functionality. For example, the software architecture 702 mayinclude layers such as an operating system 714, libraries 716,frameworks/middleware 718, applications 720, and a presentation layer744. Operationally, the applications 720 and/or other components withinthe layers may invoke API calls 724 through the software stack andreceive responses, returned values, and so forth, illustrated asmessages 726, in response to the API calls 724. The layers illustratedare representative in nature and not all software architectures have alllayers. For example, some mobile or special-purpose operating systemsmay not provide a layer of frameworks/middleware 718, while others mayprovide such a layer. Other software architectures may includeadditional or different layers.

The operating system 714 may manage hardware resources and providecommon services. The operating system 714 may include, for example, akernel 728, services 730, and drivers 732. The kernel 728 may act as anabstraction layer between the hardware and the other software layers.For example, the kernel 728 may be responsible for memory management,processor management (e.g., scheduling), component management,networking, security settings, and so on. The services 730 may provideother common services for the other software layers. The drivers 732 maybe responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 732 may include display drivers,camera drivers, Bluetooth® drivers, flash memory drivers, serialcommunication drivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi®drivers, audio drivers, power management drivers, and so forth dependingon the hardware configuration.

The libraries 716 may provide a common infrastructure that may beutilized by the applications 720 and/or other components and/or layers.The libraries 716 typically provide functionality that allows othersoftware modules to perform tasks in an easier fashion than byinterfacing directly with the underlying operating system 714functionality (e.g., kernel 728, services 730, and/or drivers 732). Thelibraries 716 may include system libraries 734 (e.g., C standardlibrary) that may provide functions such as memory allocation functions,string manipulation functions, mathematic functions, and the like. Inaddition, the libraries 716 may include API libraries 736 such as medialibraries (e.g., libraries to support presentation and manipulation ofvarious media formats such as MPEG4, H.264, MP3, AAC, AMR, JPG, PNG),graphics libraries (e.g., an OpenGL framework that may be used to render2D and 3D graphic content on a display), database libraries (e.g.,SQLite that may provide various relational database functions), weblibraries (e.g., WebKit that may provide web browsing functionality),and the like. The libraries 716 may also include a wide variety of otherlibraries 738 to provide many other APIs to the applications 720 andother software components/modules.

The frameworks 718 (also sometimes referred to as middleware) mayprovide a higher-level common infrastructure that may be utilized by theapplications 720 and/or other software components/modules. For example,the frameworks 718 may provide various graphic user interface (GUI)functions, high-level resource management, high-level location services,and so forth. The frameworks 718 may provide a broad spectrum of otherAPIs that may be utilized by e applications 720 and/or other softwarecomponents/modules, some of which may be specific to a particularoperating system or platform.

The applications 720 include built-in applications 740 and/orthird-party applications 742. Examples of representative built-inapplications 740 may include, but are not limited to, a contactsapplication, a browser application, a book reader application, alocation application, a media application, a messaging application,and/or a game application. The third-party applications 742 may includeany of the built-in applications 740 as well as a broad assortment ofother applications. In a specific example, the third-party application742 (e.g., an application developed using the Android™ or iOS™ softwaredevelopment kit (SDK) by an entity other than the vendor of theparticular platform) may be mobile software running on a mobileoperating system such as iOS™, Android™, Windows® Phone, or other mobileoperating systems. In this example, the third-party application 742 mayinvoke the API calls 724 provided by the mobile operating system such asthe operating system 714 to facilitate functionality described herein.

The applications 720 may utilize built-in operating system 714 functions(e.g., kernel 728, services 730, and/or drivers 732), libraries 716(e.g., system libraries 734, API libraries 736, and other libraries738), and frameworks/middleware 718 to create user interfaces tointeract with users of the system. Alternatively, or additionally, insome systems, interactions with a user may occur through a presentationlayer, such as the presentation layer 744. In these systems, theapplication/module “logic” can be separated from the aspects of theapplication/module that interact with a user.

Some software architectures utilize virtual machines. In the example ofFIG. 7, this is illustrated by a virtual machine 748. A virtual machinecreates a software environment where applications/modules can execute asif they were executing on a hardware machine (such as the machine 800 ofFIG. 8, for example). A virtual machine is hosted by a host operatingsystem (e.g., operating system 714 in FIG. 7) and typically, althoughnot always, has a virtual machine monitor 746, which manages theoperation of the virtual machine 748 as well as the interface with thehost operating system (e.g., operating system 714). A softwarearchitecture executes within the virtual machine 748, such as anoperating system 750, libraries 752, frameworks/middleware 754,applications 756, and/or a presentation layer 758. These layers ofsoftware architecture executing within the virtual machine 748 can bethe same as corresponding layers previously described or may bedifferent.

Example Machine Architecture and Machine-Readable Medium

FIG. 8 is a block diagram illustrating components of a machine 800,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 8 shows a diagrammatic representation of the machine800 in the example form of a computer system, within which instructions816 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 800 to perform any one ormore of the methodologies discussed herein may be executed. Theinstructions 816 transform the general, non-programmed machine into aparticular machine programmed to carry out the described and illustratedfunctions in the manner described. In alternative embodiments, themachine 800 operates as a standalone device or may be coupled (e.g.,networked) to other machines. In a networked deployment, the machine 800may operate in the capacity of a server machine or a client machine in aserver-client network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine 800 maycomprise, but not be limited to, a server computer, a client computer, aPC, a tablet computer, a laptop computer, a netbook, a set-top box(STB), a personal digital assistant (PDA), an entertainment mediasystem, a cellular telephone, a smart phone, a mobile device, a wearabledevice (e.g., a smart watch), a smart home device (e.g., a smartappliance), other smart devices, a web appliance, a network router, anetwork switch, a network bridge, or any machine capable of executingthe instructions 816, sequentially or otherwise, that specify actions tobe taken by the machine 800. Further, while only a single machine 800 isillustrated, the term “machine” shall also be taken to include acollection of machines 800 that individually or jointly execute theinstructions 816 to perform any one or more of the methodologiesdiscussed herein.

The machine 800 may include processors 810, memory/storage 830, and I/Ocomponents 850, which may be configured to communicate with each othersuch as via a bus 802. In an example embodiment, the processors 810(e.g., a Central Processing Unit (CPU), a Reduced Instruction SetComputing (RISC) processor, a Complex Instruction Set Computing (CISC)processor, a Graphics Processing Unit (GPU), a Digital Signal Processor(DSP), an ASIC, a Radio-Frequency integrated Circuit (RFIC), anotherprocessor, or any suitable combination thereof) may include, forexample, a processor 812 and a processor 814 that may execute theinstructions 816. The term “processor” is intended to include multi-coreprocessors that may comprise two or more independent processors(sometimes referred to as “cores”) that may execute instructionscontemporaneously. Although FIG. 8 shows multiple processors 810, themachine 800 may include a single processor with a single core, a singleprocessor with multiple cores (e.g., a multi-core processor), multipleprocessors with a single core, multiple processors with multiples cores,or any combination thereof.

The memory/storage 830 may include a memory 832, such as a main memory,or other memory storage, and a storage unit 836, both accessible to theprocessors 810 such as via the bus 802. The storage unit 836 and memory832 store the instructions 816 embodying any one or more of themethodologies or functions described herein. The instructions 816 mayalso reside, completely or partially, within the memory 832, within thestorage unit 836, within at least one of the processors 810 (e.g.,within the processor's cache memory), or any suitable combinationthereof, during execution thereof by the machine 800. Accordingly, thememory 832, the storage unit 836, and the memory of the processors 810are examples of machine-readable media.

As used herein, “machine-readable medium” means a device able to storeinstructions and data temporarily or permanently and may include, but isnot limited to, random-access memory (RAM), read-only memory (ROM),buffer memory, flash memory, optical media, magnetic media, cachememory, other types of storage (e.g., Erasable Programmable Read-OnlyMemory (EEPROM)), and/or any suitable combination thereof. The term“machine-readable medium” should be taken to include a single medium ormultiple media (e.g., a centralized or distributed database, orassociated caches and servers) able to store the instructions 816. Theterm “machine-readable medium” shall also be taken to include anymedium, or combination of multiple media, that is capable of storinginstructions (e.g., instructions 816) for execution by a machine (e.g.,machine 800), such that the instructions, when executed by one or moreprocessors of the machine (e.g., processors 810), cause the machine toperform any one or more of the methodologies described herein.Accordingly, a “machine-readable medium” refers to a single storageapparatus or device, as well as “cloud-based” storage systems or storagenetworks that include multiple storage apparatus or devices. The term“machine-readable medium” excludes signals per se.

The I/O components 850 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 850 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones will likely include a touch input device or other such inputmechanisms, while a headless server machine will likely not include sucha touch input device. It will be appreciated that the I/O components 850may include many other components that are not shown in FIG. 8. The I/Ocomponents 850 are grouped according to functionality merely forsimplifying the following discussion and the grouping is in no waylimiting. In various example embodiments, the I/O components 850 mayinclude output components 852 and input components 854. The outputcomponents 852 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 854 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or another pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

In further example embodiments, the I/O components 850 may includebiometric components 856, motion components 858, environmentalcomponents 860, or position components 862, among a wide array of othercomponents. For example, the biometric components 856 may includecomponents to detect expressions (e.g., hand expressions, facialexpressions, vocal expressions, body gestures, or eye tracking), measurebiosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram basedidentification), and the like. The motion components 858 may includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environmental components 860 may include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometers that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components one or more microphones thatdetect background noise), proximity sensor components (e.g., infraredsensors that detect nearby objects), gas sensors (e.g., gas detectionsensors to detect concentrations of hazardous gases for safety or tomeasure pollutants in the atmosphere), or other components that mayprovide indications, measurements, or signals corresponding to asurrounding physical environment. The position components 862 mayinclude location sensor components (e.g., a Global Position System (GPS)receiver component), altitude sensor components (e.g., altimeters orbarometers that detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 850 may include communication components 864 operableto couple the machine 800 to a network 880 or devices 870 via a coupling882 and a coupling 872, respectively. For example, the communicationcomponents 864 may include a network interface component or othersuitable device to interface with the network 880. In further examples,the communication components 864 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, Near Field Communication (NFC) components, Bluetooth®components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and othercommunication components to provide communication via other modalities.The devices 870 may be another machine or any of a wide variety ofperipheral devices (e.g., a peripheral device coupled via a USB).

Moreover, the communication components 864 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 864 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components864, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

Transmission Medium

In various example embodiments, one or more portions of the network 880may be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a WAN,a wireless WAN (WWAN), a metropolitan area network (MAN), the Internet,a portion of the Internet, a portion of the Public Switched TelephoneNetwork (PSTN), a plain old telephone service (POTS) network, a cellulartelephone network, a wireless network, a Wi-Fi® network, another type ofnetwork, or a combination of two or more such networks. For example, thenetwork 880 or a portion of the network 880 may include a wireless orcellular network and the coupling 882 may be a Code Division MultipleAccess (CDMA) connection, a Global System for Mobile communications(GSM) connection, or another type of cellular or wireless coupling. Inthis example, the coupling 882 may implement any of a variety of typesof data transfer technology, such as Single Carrier Radio TransmissionTechnology (1×RTT), Evolution-Data Optimized (EVDO) technology, GeneralPacket Radio Service (CPRS) technology, Enhanced Data rates for GSMEvolution (EDGE) technology, third Generation Partnership Project (3GPP)including 3G, fourth generation wireless (4G) networks, Universal MobileTelecommunications System (UMTS), High Speed Packet Access (HSPA),Worldwide Interoperability for Microwave Access (WiMAX), Long TermEvolution (LTE) standard, others defined by various standard-settingorganizations, other long range protocols, or other data transfertechnology.

The instructions 816 may be transmitted or received over the network 880using a transmission medium via a network interface device (e.g., anetwork interface component included in the communication components864) and utilizing any one of a number of well-known transfer protocols(e.g., HTTP). Similarly, the instructions 816 may be transmitted orreceived using a transmission medium via the coupling 872 (e.g., apeer-to-peer coupling) to the devices 870. The term “transmissionmedium” shall be taken to include any intangible medium that is capableof storing, encoding, or carrying the instructions 816 for execution bythe machine 800, and includes digital or analog communications signalsor other intangible media to facilitate communication of such software.

Language

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Although an overview of the inventive subject matter has been describedwith reference to specific example embodiments, various modificationsand changes may be made to these embodiments without departing from thebroader scope of embodiments of the present disclosure. Such embodimentsof the inventive subject matter may be referred to herein, individuallyor collectively, by the term “invention” merely for convenience andwithout intending to voluntarily limit the scope of this application toany single disclosure or inventive concept if more than one is, in fact,disclosed.

The embodiments illustrated herein are described in sufficient detail toenable those skilled in the art to practice the teachings disclosed.Other embodiments may be used and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. The Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

As used herein, the term “or” may be construed in either an inclusive orexclusive sense. Moreover, plural instances may be provided forresources, operations, or structures described herein as a singleinstance. Additionally, boundaries between various resources,operations, modules, engines, and data stores are somewhat arbitrary,and particular operations are illustrated in a context of specificillustrative configurations. Other allocations of functionality areenvisioned and may fall within a scope of various embodiments of thepresent disclosure. In general, structures and functionality presentedas separate resources in the example configurations may be implementedas a combined structure or resource. Similarly, structures andfunctionality presented as a single resource may be implemented asseparate resources. These and other variations, modifications,additions, and improvements fall within a scope of embodiments of thepresent disclosure as represented by the appended claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense.

What is claimed is:
 1. A computer-implemented method, comprising:obtaining one or more query terms in a first query; and for each of theone or more query terms: searching a standardized entity taxonomy tolocate a standardized entity that most closely matches the query term,the standardized entity taxonomy comprising an entity identification foreach of a plurality of different standardized entities; calculating aconfidence score for a query term-standardized entity pair for thestandardized entity that most closely matches the query term; inresponse to a determination that the confidence score transgresses athreshold, associating the query term with the entity identificationcorresponding to the standardized entity that most closely matches thequery term; retrieving one or more query rewriting rules correspondingto an entity type of the standardized entity having the entityidentification; and executing the one or more query rewriting rules torewrite the first query such that the rewritten query is morerestrictive than the first query.
 2. The method of claim 1, wherein theone or more query rewriting rules include adding the standardized entityhaving the entity identification to the first query with an ANDconnector.
 3. The method of claim 1, wherein the one or more queryrewriting rules include determining if the query term comprises two ormore words and, in response to a determination that the query termcomprises two or more words, adding the two or more words as a phrase tothe first query with an AND connector.
 4. The method of claim 1, whereinthe entity type is a skill and the one or more query rewriting rulesinclude determining if there are any standardized titles having anaffinity score with the standardized entity that most closely matchesthe query term higher than a preset threshold, and adding any suchstandardized titles to the first query with an AND connector.
 5. Themethod of claim 2, wherein the entity type is a skill and the one ormore query rewriting rules include determining if there are any skillssimilar to the standardized entity having the entity identification, andadding any such similar skills to the standardized entity having theentity identification to the first query with an OR connector.
 6. Themethod of claim 1, wherein the entity type is a skill and the one ormore query rewriting rules include identifying a standardized functioncorresponding to the standardized entity having the entityidentification in the standardized entity taxonomy and adding thestandardized function to the first query with an AND connector.
 7. Themethod of claim 1, wherein the confidence score indicates a statisticallikelihood that a user specifying the query term in a search query wouldhave, under ideal circumstances, also entered the correspondingstandardized entity in the search query, based on a confidence scoremodel trained via a machine learning algorithm based on member profilesand member activities in a social networking service.
 8. A systemcomprising: a computer-readable medium having instructions storedthereon, which, when executed by a processor, cause the system to:obtain one or more query terms in a first query; and for each of the oneor more query terms: search a standardized entity taxonomy to locate astandardized entity that most closely matches the query term, thestandardized entity taxonomy comprising an entity identification foreach of a plurality of different standardized entities; calculate aconfidence score for a query term-standardized entity pair for thestandardized entity that most closely matches the query term; inresponse to a determination that the confidence score transgresses athreshold, associate the query term with the entity identificationcorresponding to the standardized entity that most closely matches thequery term; retrieve one or more query rewriting rules corresponding toan entity type of the standardized entity having the entityidentification; and execute the one or more query rewriting rules torewrite the first query such that the rewritten query is morerestrictive than the first query.
 9. The system of claim 8, wherein theone or more query rewriting rules include adding the standardized entityhaving the entity identification to the first query with an ANDconnector.
 10. The system of claim 8, wherein the one or more queryrewriting rules include determining if the query term comprises two ormore words and, in response to a determination that the query termcomprises two or more words, adding the two or more words as a phrase tothe first query with an AND connector.
 11. The system of claim 8,wherein the entity type is a skill and the one or more query rewritingrules include determining if there are any standardized titles having anaffinity score with the standardized entity that most closely matchesthe query term higher than a preset threshold, and adding any suchstandardized titles to the first query with an AND connector.
 12. Thesystem of claim 9, wherein the entity type is a skill and the one ormore query rewriting rules include determining if there are any skillssimilar to the standardized entity having the entity identification, andadding any such similar skills to the standardized entity having theentity identification to the first query with an OR connector.
 13. Thesystem of claim 8, wherein the entity type is a skill and the one ormore query rewriting rules include identifying a standardized functioncorresponding to the standardized entity having the entityidentification in the standardized entity taxonomy and adding thestandardized function to the first query with an AND connector.
 14. Thesystem of claim 8, wherein the confidence score indicates a statisticallikelihood that a user specifying the query term in a search query wouldhave, under ideal circumstances, also entered the correspondingstandardized entity in the search query, based on a confidence scoremodel trained via a machine learning algorithm based on member profilesand member activities in a social networking service.
 15. Anon-transitory machine-readable storage medium comprising instructions,which when implemented by one or more machines, cause the one or moremachines to perform operations comprising: obtaining one or more queryterms in a first query; and for each of the one or more query terms:searching a standardized entity taxonomy to locate a standardized entitythat most closely matches the query term, the standardized entitytaxonomy comprising an entity identification for each of a plurality ofdifferent standardized entities; calculating a confidence score for aquery term-standardized entity pair for the standardized entity thatmost closely matches the query term; in response to a determination thatthe confidence score transgresses a threshold, associating the queryterm with the entity identification corresponding to the standardizedentity that most closely matches the query term; retrieving one or morequery rewriting rules corresponding to an entity type of thestandardized entity having the entity identification; and executing theone or more query rewriting rules to rewrite the first query such thatthe rewritten query is more restrictive than the first query.
 16. Thenon-transitory machine-readable storage medium of claim 15, wherein theone or more query rewriting rules include adding the standardized entityhaving the entity identification to the first query with an ANDconnector.
 17. The non-transitory machine-readable storage medium ofclaim 15, wherein the one or more query rewriting rules includedetermining if the query term comprises two or more words and, inresponse to a determination that the query term comprises two or morewords, adding the two or more words as a phrase to the first query withan AND connector.
 18. The non-transitory machine-readable storage mediumof claim 15, wherein the entity type is a skill and the one or morequery rewriting rules include determining if there are any standardizedtitles having an affinity score with the standardized entity that mostclosely matches the query term higher than a preset threshold, andadding any such standardized titles to the first query with an ANDconnector.
 19. The non-transitory machine-readable storage medium ofclaim 16, wherein the entity type is a skill and the one or more queryrewriting rules include determining if there are any skills similar tothe standardized entity having the entity identification, and adding anysuch similar skills to the standardized entity having the entityidentification to the first query with an OR connector.
 20. Thenon-transitory machine-readable storage medium of claim 15, wherein theentity type is a skill and the one or more query rewriting rules includeidentifying a standardized function corresponding to the standardizedentity having the entity identification in the standardized entitytaxonomy and adding the standardized function to the first query with anAND connector.